10 research outputs found
Message-Passing Algorithms for Channel Estimation and Decoding Using Approximate Inference
We design iterative receiver schemes for a generic wireless communication
system by treating channel estimation and information decoding as an inference
problem in graphical models. We introduce a recently proposed inference
framework that combines belief propagation (BP) and the mean field (MF)
approximation and includes these algorithms as special cases. We also show that
the expectation propagation and expectation maximization algorithms can be
embedded in the BP-MF framework with slight modifications. By applying the
considered inference algorithms to our probabilistic model, we derive four
different message-passing receiver schemes. Our numerical evaluation
demonstrates that the receiver based on the BP-MF framework and its variant
based on BP-EM yield the best compromise between performance, computational
complexity and numerical stability among all candidate algorithms.Comment: Accepted for publication in the Proceedings of 2012 IEEE
International Symposium on Information Theor
Merging Belief Propagation and the Mean Field Approximation: A Free Energy Approach
We present a joint message passing approach that combines belief propagation
and the mean field approximation. Our analysis is based on the region-based
free energy approximation method proposed by Yedidia et al. We show that the
message passing fixed-point equations obtained with this combination correspond
to stationary points of a constrained region-based free energy approximation.
Moreover, we present a convergent implementation of these message passing
fixedpoint equations provided that the underlying factor graph fulfills certain
technical conditions. In addition, we show how to include hard constraints in
the part of the factor graph corresponding to belief propagation. Finally, we
demonstrate an application of our method to iterative channel estimation and
decoding in an orthogonal frequency division multiplexing (OFDM) system